Brain-computer interface prototype to support upper limb rehabilitation processes in the human body (original) (raw)
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MOTOR IMAGERY BCI SYSTEM WITH VISUAL FEEDBACK: DESIGN AND PRELIMINARY EVALUATION
— Nowadays, strokes are a growing cause of mortality and many people remain with motor sequelae and troubles in the daily activities. To treat these sequelae, alternative rehabilitation techniques are needed. This article describes the design, development and preliminary evaluation of a system based on Brain Computer Interfaces (BCI) by Motor Imagery, with visual feedback for lower limb rehabilitation of people post stroke. The system consists of three modules: Sensing and Conditioning; Control Signal Generator; and Visual Feedback. The first module acquires, filters and segments 5 channels of EEG. The second module performs spatial filtering using a Laplacian, estimates the signal power spectral density, extracts and selects EEG features which are then used by the classifier to detect event related desynchronization. The command signal generated by the BCI is inputted into the third module, which simulates the movement of foot dorsiflexion of an avatar displayed on a screen. For the implementation, the BCI2000, V-REP platforms and MATLAB software were used. Performance evaluation of the system was done in a healthy volunteer by estimating the sensitivity and specificity, and through interviews with specialists. Average values for sensitivity and specificity were 0,67 and 0,70 respectively, and professional opinions were very good. These results are encouraging for deepening the performance evaluation system and taking steps for clinical implementation.
Neuroscience and Behavioral Physiology, 2018
Neurocomputer interfaces or, as they have come to be known in the Russian literature, brain-computer interfaces (BCI), are used in several areas and have the potential for uses in solving both research and applied tasks. Pilot studies in the clinical application of BCI to poststroke neurorehabilitation are currently under way [Frolov et al., 2013; Ang et al., 2010], and there are prospects for the use of BCI for direct restoration of movement/communication capabilities by creating an alternative information exchange channel with intelligent prostheses and the surroundings. Studies using electrophysiological data generate the need to process multidimensional, nonstationary signals, refl ecting complex physiological processes. Interfaces based on noninvasive technologies for recording brain activity do not as yet provide reliable information links with the user's brain. The results of our studies show that improvements in the working characteristics of these systems can be obtained by constructing new machine learning algorithms considering the physiological and psychoemotional characteristics of BCI use. These algorithms can be developed either in the classical Bayesian paradigm or using state-of-the-art deep learning techniques. In addition, the creation of methods for the physiological interpretation of nonlinear decision rules found by multilayered structures opens up new potentials for the automatic and objective extraction of knowledge from experimental neurophysiological data. Despite the attractiveness of noninvasive technologies, radical increases in the throughput of BCI communication channels and the use of this technology to control prostheses can only be obtained using invasive methods of recording brain activity. Electrocorticograms (ECoG) are the least invasive of these technologies, and in the concluding part of this work we will demonstrate that ECoG can be used for decoding of the kinematic characteristics of fi nger movements.
Over 2 million people are affected by neural diseases such as multiple Sclerosis, Amyotrophic Lateral Sclerosis, spinal cord injury, cerebral palsy, and other diseases impairing the neural pathways that control muscles. Indeed, these diseases cause severe paralysis and the persons suffer from what is called "Locked in syndrom". Consequently, a Brain Computer Interface noted BCI can be used as an alternative communication channel. This project belongs to a Brain Computer Interface research. More precisely, it focuses on the development of noninvasive platform of electroencephalographic (EEG) signals in terms of acquisition, pre-processing, feature extraction for providing an alternative communication or control channel for patient with severe motor disabilities.
Use of brain computer interfaces in neurological rehabilitation
2011
Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more eff ective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the effi cacy of a rehabilitation protocol and thus improve muscle control for the patient.
On Usage Of EEG Brain Control For Rehabilitation Of Stroke Patients
ECMS 2016 Proceedings edited by Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose, 2016
This paper demonstrates rapid prototyping of a stroke rehabilitation system consisting of an interactive 3D virtual reality computer game environment interfaced with an EEG headset for control and interaction using brain waves. The system is intended for training and rehabilitation of partially monoplegic stroke patients and uses lowcost commercial-off-the-shelf products like the Emotiv EPOC EEG headset and the Unity 3D game engine. A number of rehabilitation methods exist that can improve motor control and function of the paretic upper limb in stroke survivors. Unfortunately, most of these methods are commonly characterised by a number of drawbacks that can limit intensive treatment, including being repetitive, uninspiring, and labour intensive; requiring one-on-one manual interaction and assistance from a therapist, often for several weeks; and involve equipment and systems that are complex and expensive and cannot be used at home but only in hospitals and institutions by trained personnel. Inspired by the principles of mirror therapy and game-stimulated rehabilitation, we have developed a first prototype of a game-like computer application that tries to avoid these drawbacks. For rehabilitation purposes, we deprive the patient of the view of the paretic hand Corresponding author: Robin T. Bye. while being challenged with controlling a virtual hand in a simulated 3D game environment only by means of EEG brain waves interfaced with the computer. Whilst our system is only a first prototype, we hypothesise that by iteratively improving its design through refinements and tuning based on input from domain experts and testing on real patients, the system can be tailored for being used together with a conventional rehabilitation programme to improve patients' ability to move the paretic limb much in the same vain as mirror therapy. Our proposed system has several advantages, including being game-based, customisable, adaptive, and extendable. In addition, when compared with conventional rehabilitation methods, our system is extremely low-cost and flexible, in particular because patients can use it in the comfort of their homes, with little or no need for professional human assistance. Preliminary tests are carried out to highlight the potential of the proposed rehabilitation system, however, in order to measure its efficiency in rehabilitation, the system must first be improved and then run through an extensive field test with a sufficiently large group of patients and compared with a control group.
Brain-computer interface for virtual reality control
2009
An electroencephalogram (EEG) based brain-computer interface (BCI) was connected with a Virtual Reality system in order to control a smart home application. Therefore special control masks were developed which allowed using the P300 component of the EEG as input signal for the BCI system. Control commands for switching TV channels, for opening and closing doors and windows, for navigation and conversation were realized. Experiments with 12 subjects were made to investigate the speed and accuracy that can be achieved if several hundred of commands are used to control the smart home environment. The study clearly shows that such a BCI system can be used for smart home control. The Virtual Reality approach is a very cost effective way for testing the smart home environment together with the BCI system.
Brain Computer Interfaces, a Review
A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
2013
BRAIN COMPUTER INTERFACE BASED NEUROREHABILITATION TECHNIQUE USING A COMMERCIALLY AVAILABLE EEG HEADSET by Abhineet Mishra Neurorehabilitation has recently been augmented with the use of virtual reality and rehabilitation robotics. In many systems, some known volitional control must exist in order to synchronize the user intended movement with the therapeutic virtual or robotic movement. Brain Computer Interface (BCI) aims to open up a new rehabilitation option for clinical population having no residual movement due to disease or injury to the central or peripheral nervous system. Brain activity contains a wide variety of electrical signals which can be acquired using many invasive and non-invasive acquisition techniques and holds the potential to be used as an input to BCI. Electroencephalogram (EEG) is a non-invasive method of acquiring brain activity which then, with further processing and classification, can be used to predict various brain states such as an intended motor movem...
A REVIEW ON BRAIN COMPUTER INTERFACE
A brain-computer interface (BCI) establishes a link between the human brain and the external devices. BCIs measure the brain activity for fetching the user’s intent and subsequently provide the control signals to the supporting hardware. This technology has varied uses ranging from assistive devices for disabled individuals to advanced simulator control. The main use of BCI is as an assistive technology for individuals suffering from loss of motor control caused by spinal cord injury, amyotrophic lateral sclerosis or any other possible incidence. BCIs take advantage of the brain’s electrochemical signals. There are billions of neurons in human brain with trillions of interconnections known as synapses. These devices also make use of neuroplasticity which is the brain’s ability to change physically and functionally over time. Author has discussed the basics of BCI in this paper and has presented details regarding brain waves, control centers of various organs in brain, invasive and non-invasive sensors. This paper also presents a summary of the research work going on in this area.
Brain–computer interfaces in neurological rehabilitation
The Lancet Neurology, 2008
Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more eff ective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the effi cacy of a rehabilitation protocol and thus improve muscle control for the patient.